American Indians and COVID-19: Morbidity and Mortality Disparities among Indigenous Populations in the Rural South

Background The COVID-19 pandemic has highlighted health inequities among indigenous populations, with those in rural settings facing compounded barriers.Purpose To investigate morbidity and mortality experiences among hospitalized, COVID-19+ American Indian adults from rural and urban settings.Methods The described cross-sectional study used retrospective discharge data from the University of Mississippi Medical Center and Hennepin County Medical Center. Adults (≥ age 18) admitted from January 1, 2020 to August 8, 2021with a COVID-19 diagnosis and known race were included.Results A total of 3,659 inpatients met inclusion criteria. Among adults hospitalized with COVID-19 at the University of Mississippi Medical Center, American Indians (n=73) had the highest mean comorbidity risk score (11.2, SD 8.1) and unadjusted mortality rate (42%) among all races. Among adults hospitalized with COVID-19 at Hennepin County Medical Center, American Indians (n=62) had the second lowest comorbidity risk score (6.1, SD 10.7) and the lowest unadjusted mortality rate (6%). American Indian mortality disparities persisted after controlling for age, sex, and comorbidity risk.Conclusion Hospitalized American Indians from predominantly rural settings experienced significant morbidity and COVID-19 mortality disparities when compared to native persons in predominantly urban environments, or Blacks and Whites in either setting. Compounded disparities faced by rural, indigenous populations must be addressed

Effects of Age and Functional Status on the Relationship of Systolic Blood Pressure With Mortality in Mid and Late Life: The ARIC Study

Impaired functional status attenuates the relationship of systolic blood pressure (SBP) with mortality in older adults but has not been studied in middle-aged populations

Expression of poly-ADP-ribose polymerase (PARP) in endometrial adenocarcinoma: Prognostic potential

© 2020 Background: In the United States endometrial carcinoma is the most common female gynecologic malignancy. An average of more than 60,000 new cases of endometrial carcinomas have been diagnosed yearly over the past 5 years, with a higher incidence occurring in the central Appalachian states of Ohio and West Virginia. In the U.S., the national average of newly diagnosed endometrial carcinomas is 26.8 in every 100,000 women, while in the states of Ohio and West Virginia the average is 30.5 and 31.1 in every 100,000 women, respectively. This notable increase in the incidence of endometrial carcinomas may be due a variety of elevated risk factors including but not limited to: tobacco use, obesity, and genetic predisposition of the predominant demographic. The American Cancer Society estimates that approximately 55,000 new cases of endometrial carcinoma will be diagnosed in 2020 yet, this disease is widely considered understudied and under-represented in mainstream cancer research circles. Methods: The aim of this study was to quantitate the co-expression of two DNA repair proteins poly-ADP-ribose polymerase 1 and 2 (Parp-1 and Parp-2) by enzyme- linked immuno-sorbent assay (ELISA) in 60 endometrioid endometrial tumor samples and compare their expression to matched non-malignant endometrial tissue from the same corresponding donors from central Appalachia. Results: We found that Parp-1 was significantly overexpressed in endometrial carcinoma relative to corresponding normal tissue. This overexpression implicates Parp inhibition therapy as a possible treatment for the disease. Our results also found a protective effect of native Parp-2 expression in non-malignant endometrial tissue with each 1 ng/mL increase in PARP-2 concentration in normal tissue was associated with a 10 % reduction in the hazard of tumor progression (HR = 0.90; p = 0.039) and a 21 % reduction in the hazard of death (HR = 0.79; p = 0.044). Conclusions: This study demonstrated the over-expression of the druggable target Parp-1 in endometrial adenocarcinoma and observed a strong negative correlation of native Parp-2 expression and disease progression via the quantification of the Parp proteins using enzyme- linked immuno-sorbent assay (ELISA) assays

Clinical relevance of cancer stem cell chemotherapeutic assay for recurrent ovarian cancer

© 2020 Introduction: Disease recurrence and progression of ovarian cancer is common with the development of platinum-resistant or refractory disease. This is due in large part to the presence of chemo-resistant cancer stem cells (CSCs) that contribute to tumor propagation, maintenance, and treatment resistance. We developed a CSCs drug cytotoxicity assay (ChemoID) to identify the most effective chemotherapy treatment from a panel of FDA approved chemotherapies. Methods: Ascites and pleural fluid samples were collected under physician order from 45 consecutive patients affected by 3rd-5th relapsed ovarian cancer. Test results from the assay were used to treat patients with the highest cell kill drugs, taking into consideration their health status and using dose reductions, as needed. A retrospective chart review of CT and PET scans was used to determine patients\u27 outcomes for tumor response, time to recurrence, progression-free survival (PFS), and overall survival (OS). Results: We observed that recurrent ovarian cancer patients treated with high-cell kill chemotherapy agents guided by the CSCs drug response assay had an improvement in the median PFS corresponding to 5.4 months (3rd relapse), 3.6 months (4th relapse), and 3.9 months (5th relapse) when compared to historical data. Additionally, we observed that ovarian cancer patients identified as non-responders by the CSC drug response assay had 30 times the hazard of death compared to those women that were identified as responders with respective median survivals of 6 months vs. 13 months. We also found that ChemoID treated patients on average had an incremental cost-effectiveness ratio (ICER) between -$18,421 and$7,241 per life-year saved (LYS). Conclusions: This study demonstrated improved PFS and OS for recurrent ovarian cancer patients treated with assay-guided chemotherapies while decreasing the cost of treatment

Following the Science: the role of the academic medical center in the COVID-19 pandemic

This article reviews the experience and role of an academic medical center in a response to the COVID-19 pandemic

Cancer Stem Cell Chemotherapeutics Assay for Prospective Treatment of Recurrent Glioblastoma and Progressive Anaplastic Glioma: A Single-Institution Case Series

© 2020 BACKGROUND: Chemotherapy-resistant cancer stem cells (CSC) may lead to tumor recurrence in glioblastoma (GBM). The poor prognosis of this disease emphasizes the critical need for developing a treatment stratification system to improve outcomes through personalized medicine. METHODS: We present a case series of 12 GBM and 2 progressive anaplastic glioma cases from a single Institution prospectively treated utilizing a CSC chemotherapeutics assay (ChemoID) guided report. All patients were eligible to receive a stereotactic biopsy and thus undergo ChemoID testing. We selected one of the most effective treatments based on the ChemoID assay report from a panel of FDA approved chemotherapy as monotherapy or their combinations for our patients. Patients were evaluated by MRI scans and response was assessed according to RANO 1.1 criteria. RESULTS: Of the 14 cases reviewed, the median age of our patient cohort was 49 years (21–63). We observed 6 complete responses (CR) 43%, 6 partial responses (PR) 43%, and 2 progressive diseases (PD) 14%. Patients treated with ChemoID assay-directed therapy, in combination with other modality of treatment (RT, LITT), had a longer median overall survival (OS) of 13.3 months (5.4-NA), compared to the historical median OS of 9.0 months (8.0–10.8 months) previously reported. Notably, patients with recurrent GBM or progressive high-grade glioma treated with assay-guided therapy had a 57% probability to survive at 12 months, compared to the 27% historical probability of survival observed in previous studies. CONCLUSIONS: The results presented here suggest that the ChemoID Assay has the potential to stratify individualized chemotherapy choices to improve recurrent and progressive high-grade glioma patient survival. Importance of the Study: Glioblastoma (GBM) and progressive anaplastic glioma are the most aggressive brain tumor in adults and their prognosis is very poor even if treated with the standard of care chemoradiation Stupp\u27s protocol. Recent knowledge pointed out that current treatments often fail to successfully target cancer stem cells (CSCs) that are responsible for therapy resistance and recurrence of these malignant tumors. ChemoID is the first and only CLIA (clinical laboratory improvements amendment) -certified and CAP (College of American Pathologists) -accredited chemotherapeutic assay currently available in oncology clinics that examines patient\u27s derived CSCs susceptibility to conventional FDA (Food and Drugs Administration) -approved drugs. In this study we observed that although the majority of our patients (71.5%) presented with unfavorable prognostic predictors (wild type IDH-1/2 and unmethylated MGMT promoter), patients treated with ChemoID assay-directed therapy had an overall response rate of 86% and increased median OS of 13.3 months compared to the historical median OS of 9.1 months (8.1–10.1 months) previously reported [1] suggesting that the ChemoID assay may be beneficial in personalizing treatment strategies

Cancer Stem Cell Assay-Guided Chemotherapy Improves Survival of Patients With Recurrent Glioblastoma in a Randomized Trial

Therapy-resistant cancer stem cells (CSCs) contribute to the poor clinical outcomes of patients with recurrent glioblastoma (rGBM) who fail standard of care (SOC) therapy. ChemoID is a clinically validated assay for identifying CSC-targeted cytotoxic therapies in solid tumors. In a randomized clinical trial (NCT03632135), the ChemoID assay, a personalized approach for selecting the most effective treatment from FDA-approved chemotherapies, improves the survival of patients with rGBM (2016 WHO classification) over physician-chosen chemotherapy. In the ChemoID assay-guided group, median survival is 12.5 months (95% confidence interval [CI], 10.2-14.7) compared with 9 months (95% CI, 4.2-13.8) in the physician-choice group (p = 0.010) as per interim efficacy analysis. The ChemoID assay-guided group has a significantly lower risk of death (hazard ratio [HR] = 0.44; 95% CI, 0.24-0.81; p = 0.008). Results of this study offer a promising way to provide more affordable treatment for patients with rGBM in lower socioeconomic groups in the US and around the world

Complete automation of a participant characteristics table

Most published research that contains real-world data displays a table of participant characteristics, baseline characteristics, or demographics. I present a program, partchart, that provides automatic output of the participant characteristics table in multiple formats and gives the user control over formatting, thus facilitating complete reproducibility

Capturing a Stata dataset and releasing it into REDCap

With technology advances, researchers can now capture data using web-based applications. One such application, Research Electronic Data Capture (REDCap), allows for data entry from any computer with an Internet connection. As the use of REDCap has increased in popularity, we have observed the need to easily create data dictionaries and data collection instruments for REDCap. The command presented in this article, redcapture, demonstrates one method to create a REDCap-ready data dictionary using a loaded Stata dataset, illustrated by examples of starting from an existing dataset or completely starting from scratch

Capturing a Stata dataset and releasing it into REDCap

With technology advances, researchers can now capture data using web-based applications. One such application, Research Electronic Data Capture (REDCap), allows for data entry from any computer with an Internet connection. As the use of REDCap has increased in popularity, we have observed the need to easily create data dictionaries and data collection instruments for REDCap. The command presented in this article, redcapture, demonstrates one method to create a REDCap-ready data dictionary using a loaded Stata dataset, illustrated by examples of starting from an existing dataset or completely starting from scratch